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Publication
Análise de dados na compreensão do comportamento de compra online: Caso de estudo na La Redoute Portugal
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 6.15 MB | Adobe PDF |
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Abstract(s)
No ambiente competitivo atual, a análise de dados é fundamental para as empresas,
pois permite compreender os comportamentos de compra dos consumidores e, assim, tomar
decisões estratégicas que impulsionam o crescimento e a eficiência dos negócios. Ao coletar
e examinar informações sobre preferências e tendências, as organizações conseguem
identificar padrões que otimizam suas estratégias de marketing e vendas. Isso permite que
as marcas personalizem as suas ofertas e melhorem a experiência do cliente, proporcionando
insights valiosos sobre o que motiva as decisões de compra.
Além disso, a sazonalidade desempenha um papel importante nessa análise, uma vez
que os comportamentos de compra variam ao longo do ano, influenciados por datas
comemorativas e eventos especiais. Compreender essas flutuações permite às empresas
antecipem a procura, ajustem stocks e implementem campanhas de marketing eficazes.
O principal objetivo deste estudo é analisar se a integração de dados internos e externos
permitem identificar padrões sazonais de consumo em produtos para casa no mercado de e commerce. Para isso, é fundamental reconhecer esses padrões no comportamento dos
consumidores, analisando a relação entre os dados recolhidos e verificando a existência de
correlação, através da aplicação da correlação de Pearson. Dessa forma, torna-se essencial a
criação de um mapa de sazonalidade, que facilite a interpretação dos dados recolhidos e
possibilite uma análise mais eficaz de toda a informação processada.
Os resultados deste estudo confirmaram que a integração de dados internos e externos
é essencial para identificar padrões sazonais de consumo em produtos para a categoria “casa”
no mercado online, evidenciando uma correlação entre os dados analisados. Além disso, foi
possível observar que diferentes produtos apresentam sazonalidades distintas, exigindo
estratégias ajustadas. No entanto, é importante considerar que fatores como ações
comerciais, nomeadamente promoções e alterações económicas podem influenciar
significativamente esses padrões sazonais.
In today's competitive environment, data analytics is critical for businesses to understand consumer buying patterns and make strategic decisions that drive growth and efficiency. Data analytics is essential for businesses to understand consumer buying patterns. By collecting and studying information about preferences and trends, companies can identify patterns that optimize their marketing and sales strategies. This allows brands to personalize their offerings and improve the customer experience, providing valuable insights into what drives purchase decisions. Seasonality also plays an important role in this analysis, as buying patterns fluctuate throughout the year, influenced by holidays and special events. Understanding these variations enables companies to anticipate demand, adjust inventory levels and implement effective marketing campaigns. The main objective of this study is to analyse whether the integration of internal and external data allows the identification of seasonal consumption patterns of household products in the e-commerce market. To achieve this, it is essential to identify these patterns in consumer behavior by analyzing the relationship between the data collected and verifying the existence of a correlation using Pearson's correlation analysis. The creation of a seasonality map is therefore crucial, as it facilitates the interpretation of the data collected and allows a more effective analysis of all the information processed. The results of this study confirmed that the integration of internal and external data is essential to identify seasonal consumption patterns of household products in the online market, highlighting a correlation between the data analyzed. Different products exhibit distinct seasonal trends, requiring tailored strategies. However, it is important to bear in mind that factors such as commercial actions, in particular promotions, and economic changes can have a significant impact.
In today's competitive environment, data analytics is critical for businesses to understand consumer buying patterns and make strategic decisions that drive growth and efficiency. Data analytics is essential for businesses to understand consumer buying patterns. By collecting and studying information about preferences and trends, companies can identify patterns that optimize their marketing and sales strategies. This allows brands to personalize their offerings and improve the customer experience, providing valuable insights into what drives purchase decisions. Seasonality also plays an important role in this analysis, as buying patterns fluctuate throughout the year, influenced by holidays and special events. Understanding these variations enables companies to anticipate demand, adjust inventory levels and implement effective marketing campaigns. The main objective of this study is to analyse whether the integration of internal and external data allows the identification of seasonal consumption patterns of household products in the e-commerce market. To achieve this, it is essential to identify these patterns in consumer behavior by analyzing the relationship between the data collected and verifying the existence of a correlation using Pearson's correlation analysis. The creation of a seasonality map is therefore crucial, as it facilitates the interpretation of the data collected and allows a more effective analysis of all the information processed. The results of this study confirmed that the integration of internal and external data is essential to identify seasonal consumption patterns of household products in the online market, highlighting a correlation between the data analyzed. Different products exhibit distinct seasonal trends, requiring tailored strategies. However, it is important to bear in mind that factors such as commercial actions, in particular promotions, and economic changes can have a significant impact.
Description
Keywords
Análise de dados Comportamento do consumidor E-Commerce Sazonalidade